Most of human knowledge has been recorded and stored by textual compositions or can be converted to textual compositions. The information in written texts and compositions has been used in traditional way by individual researchers and professionals to draw useful conclusions about the desired task or goals or applications. However, in these day and age that data is generated at an unprecedented rate it is very hard for a human operator to analyze these large bodies of data in order to extract the real information and knowledge therein and using them to further advance the state of knowledge or discovery of a real knowledge about any subject matter.
For example for any topic or subject there are vast amount of textual repositories such as collection of research papers in any particular topic or subject, news feeds, interviews, talks, video collections and the like. Gaining any benefit from such unstructured collections of information needs lots of expertise, time, and many years of training just even to separate the facts and value out of these immense amounts of data. Not every piece of data is worthy of attention and investigation or investment of expensive times of experts and professionals.
Moreover, there is no guarantee that a human investigator or researcher can accurately analyze the vast collection of documents and information. The results of the investigations are usually biased by the individual's knowledge, experiences, and background. The complexities of relations in the bodies of data limit the throughputs of knowledge-based professionals and the speed at which credible knowledge can be produced. The desired speed or rate of knowledge discovery apparently is much higher than the present rate of knowledge discovery and production.
Therefore, there is a need to enhance the art of knowledge discovery in terms of accuracy, speed and throughput.